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Robust estimation approach for spatial error model
Journal of Statistical Computation and Simulation ( IF 1.2 ) Pub Date : 2020-03-23 , DOI: 10.1080/00949655.2020.1740223
Vural Yildirim 1 , Yeliz Mert Kantar 2
Affiliation  

Spatial regression models, used to model spatial relationships, have received considerable attention in recent years since numerous data sets have been collected with geographical references in space. The spatial error model (SEM), among spatial regression models, has been widely applied for spatial data in the literature due to its simple structure. However, it is known that the classical estimation methods such as the maximum likelihood and generalized moment can be influenced by the presence of outliers in the data. In this article, a robust estimation approach based on the robustified likelihood equations for SEM is proposed. The results of the simulation study show that the proposed estimator for SEM has smaller bias and mean squared errors and exhibits more robust empirical influence function than the classical methods, when there are outliers in the dataset. The results of all analysis show that the proposed estimator is robust to outliers.

中文翻译:

空间误差模型的鲁棒估计方法

用于建模空间关系的空间回归模型近年来受到了相当大的关注,因为已经收集了大量具有空间地理参考的数据集。空间回归模型中的空间误差模型(SEM)由于其结构简单,在文献中被广泛应用于空间数据。然而,众所周知,最大似然和广义矩等经典估计方法会受到数据中存在异常值的影响。在本文中,提出了一种基于 SEM 的稳健似然方程的稳健估计方法。模拟研究的结果表明,所提出的 SEM 估计量具有更小的偏差和均方误差,并且比经典方法表现出更稳健的经验影响函数,当数据集中存在异常值时。所有分析的结果表明,所提出的估计量对异常值具有鲁棒性。
更新日期:2020-03-23
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